Home' Army Acquisition Logistics and Technology Magazine : Army ALT April-June 2014 Contents involved in the complexities of acquisi-
tion, but it resonates particularly with
the AMSAA risk IPT. Team members try
continuously to enhance their risk assess-
ment approaches to ensure that they are
reporting the significant ramifications of
all critical sources of schedule risk in a
realistic, unbiased and rational manner.
Any method of executing a risk assessment
must be supportable in the time frame
allotted by AoA guidance. Methods also
must be consistent and repeatable for each
new AoA. The main objective is to deliver
the most useful risk information possible
to decision-makers so they can make fully
informed decisions that will lead to bal-
anced costs, benefits and prudent risks.
The risk team faces the challenges of
increasing the quantity of objective data
available for risk assessments, and ensur-
ing the data’s quality with respect to using
the information within a model. The team
continues its research to establish a better
understanding of the critical factors that
create schedule risk and a clearer picture of
how best to assess those risks through the
use of historical data and SME judgments.
The risk IPT continuously seeks to tap
the expertise of other acquisition orga-
nizations to help develop risk assessment
models. With this kind of collaboration,
the team will proceed toward its goal of
providing the best possible product—an
independent, honest and accurate sched-
ule risk assessment.
David Vose, a consultant in risk analy-
sis, noted in his book “Risk Analysis: A
Quantitative Guide” that “The biggest
uncertainty in risk analysis is whether we
started off analyzing the right thing and
in the right way.” The AMSA A risk team,
accustomed to addressing this uncer-
tainty, is preparing to embark on more
model research and development activi-
ties to improve the quality of its methods.
Many challenges still need to be addressed.
In due course, the AMSAA team aims to
be a strong link in the chain of all the
dedicated analysts who are serving to
make better acquisition decisions to
safeguard and equip the warfighter.
For more information, go to http://web.
html or contact the leader of AMSA A’s
risk team, Suzanne Singleton, at
MR. TIMOTHY E. BISCOE is an
operations research analyst at AMSAA,
Aberdeen Proving Ground (APG), MD.
He holds an M.S . in project management
with a concentration in operations research
from the Florida Institute of Technology
and a B.S . in mathematics with a concen-
tration in statistics from the University of
Maryland. He is Level II certified in engi-
neering and is a member of the U.S . Army
Acquisition Corps (A AC).
MR. ANDREW B. CLARK is a computer
scientist at AMSAA. He holds an M.S . in
computer science from Johns Hopkins Uni-
versity and a B.S . in computer science from
Towson University. He is Level II certified
in engineering and is a member of the AAC.
MR. JOHN S. NIERWINSKI is a math-
ematician and statistician at AMSAA. He
is also an adjunct professor at the Florida
Institute of Technology, APG campus. He
holds an M.S . in operations research from
the Florida Institute of Technology and
a B.S . in mathematics with a concentra-
tion in actuarial sciences from Towson
University. He also holds U.S . Patent No.
8,335,660, issued Dec. 18, 2012. He has
authored professional journal articles and
numerous technical reports on various
research topics and methodologies. He is
Level II certified in engineering and is a
member of the A AC.
SRDDM lower confidence bound
SREDM Model 1 (Level 1)
SREDM Model 1 (Level 2)
SREDM Model 1 (Level 3)
SREDM Model 2 (0% Delays)
SREDM Model 2 (50% Delays)
SREDM Model 3
SRDDM AND SREDM MODEL OUTPUTS
This conceptual plot shows the probability that a notional new-vehicle program will complete
its EMD phase within the 50 months allotted in the PM’s program schedule. Each curve plotted
in the graph represents a different model and excursion. Model 1 represents simulations using
only historical event data. The three excursions within Model 1 (Levels 1, 2 and 3) refer to the
schedule events included in the modeling; each increase in level represents the addition of more
detailed schedule events. Model 2 used historical delay data to show the effect of potential delays
on a schedule. Of the two notional Model 2 excursions, delays had no chance of occurring in
one, whereas each delay had a 50 percent chance of occurring in the other. Model 3 represents
simulations that combined SME input and historical event data. (SOURCE: AMSAA risk IPT)
Army AL&T Magazine
Links Archive Army ALT January-March 2014 Army ALT July-September 2014 Navigation Previous Page Next Page